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Record W2044814276 · doi:10.1890/09-2346.1

Stochastic growth reduces population fluctuations in<i>Daphnia</i>–algal systems

2010· article· en· W2044814276 on OpenAlexaff
Bharath Ananthasubramaniam, Roger M. Nisbet, William A. Nelson, Edward McCauley, William S. C. Gurney

Bibliographic record

VenueEcology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicAquatic Ecosystems and Phytoplankton Dynamics
Canadian institutionsQueen's University
Fundersnot available
KeywordsDaphnia pulexDaphniaEcologyRobustness (evolution)BiologyPopulationPopulation modelPopulation growthEconometricsMathematicsZooplankton

Abstract

fetched live from OpenAlex

Deterministic, size-structured models are widely used to describe consumer-resource interactions. Such models typically ignore potentially large random variability in juvenile development rates. We present simple representations of this variability and show five approaches to calculating the model parameters for Daphnia pulex interacting with its algal food. Using our parameterized models of growth variability, we investigate the robustness of a recently proposed stabilizing mechanism for Daphnia populations. Growth rate variability increases the range of enrichments over which small-amplitude cycles or quasi-cycles occur, thus increasing the plausibility that the underlying mechanism contributes to the prevalence of small-amplitude cycles in the field and in experiments. More generally, our approach allows us to relate commonly available information on variance of development times to population stability.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.212
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations20
Published2010
Admission routes1
Has abstractyes

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